Bayesian robust estimation of partially functional linear regression models using heavy-tailed distributions

2020 ◽  
Vol 35 (4) ◽  
pp. 2077-2092
Author(s):  
Guodong Shan ◽  
Yiheng Hou ◽  
Baisen Liu
2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Xuedong Chen ◽  
Qianying Zeng ◽  
Qiankun Song

An extension of some standard likelihood and variable selection criteria based on procedures of linear regression models under the skew-normal distribution or the skew-tdistribution is developed. This novel class of models provides a useful generalization of symmetrical linear regression models, since the random term distributions cover both symmetric as well as asymmetric and heavy-tailed distributions. A generalized expectation-maximization algorithm is developed for computing thel1penalized estimator. Efficacy of the proposed methodology and algorithm is demonstrated by simulated data.


2020 ◽  
Vol 152 ◽  
pp. 107041
Author(s):  
Graciela Boente ◽  
Matías Salibian-Barrera ◽  
Pablo Vena

2021 ◽  
Vol 14 (4) ◽  
pp. 359-371
Author(s):  
Zhiqiang Jiang ◽  
Zhensheng Huang ◽  
Hanbing Zhu

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